Quality Control Concepts for LiDAR
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1 Quality Control Concepts for LiDAR January 24, 2012 Engineering Architecture Design-Build Surveying GeoSpatial Solutions
2 Presentation Objectives Offer realistic and constructive advice for LiDAR project quality control (QC) issues Educate end-users and geospatial managers about technical challenges associated with airborne LiDAR data collection and processing activities Lecture or conversation feel free to ask questions! PREXXXX 2
3 Presenter Bio Bill Emison Senior Account Mgr. / MARS Product Mgr. (GeoSpatial Solutions) Geospatial professional since 1993 Professional experience includes solutions consulting, software / service sales and technical marketing B.S., Business Administration, Univ. of Louisville, Louisville, KY, 1991 M.S., City Planning (GIS), Georgia Institute of Technology, Atlanta, GA, 1997 PREXXXX 3
4 Presenter Bio Merinda Lobato LiDAR Project Coordinator In the mapping industry since 1999, working with LiDAR data since 2000 Professional experience includes LiDAR operations and processing, surveying, GPS / IMU processing, boresighting, LiDAR filtering, and LiDAR derivative production Highly experienced in LiDAR data processing software (MARS ) PREXXXX 4
5 Corporate Information Corporate headquarters: Aurora, Colorado Founded in 1955; employee-owned $115M annual revenue (FY11) 500 employees at 10 national / 3 int l offices Market Focus Energy Security Life Sciences Infrastructure Business Units GeoSpatial Solutions Civil Engineering Solutions Military / Gov t Facilities Fuels & Energy Science & Technology Nuclear Services & Technology PREXXXX 5
6 Office Locations Aurora, CO Headquarters Ottawa, Canada Colorado Springs, CO Los Alamos, NM Albuquerque, NM Charlotte, NC Oak Ridge, TN Duluth, GA Atlanta, GA San Antonio, TX Guadalajara, Mexico (MAPA) Mexico City, Mexico (MAPA) PREXXXX 6
7 LiDAR Collection Platforms PREXXXX 7
8 Raw LiDAR LiDAR is collected in a proprietary format, based on the sensor s manufacturer. This data is typically referred to as raw (unprocessed) LiDAR point cloud data. Sensor manufacturers have their own post-processing software that combine raw scan data with GPS (position) data and IMU (orientation) data to produce a LiDAR file (LAS). At this point, the point cloud data is dumb no data classifications have been assigned; typically organized by individual flight lines PREXXXX 8
9 ASPRS LAS Specification The LAS file format is a public file format for the interchange of 3D point cloud data between users. (as defined by ASPRS) Developed by ASPRS in conjunction with LiDAR vendors and industry members of the ASPRS Standards Committee Binary format (smaller); high performance Includes all relevant LiDAR attributes LAS Versions 1.0, 1.1, 1.2, 1.3, exchange_format.html PREXXXX 9
10 LAS Header Information (File Properties) PREXXXX 10
11 LAS Single Point Properties PREXXXX 11
12 Projection Information in LAS Projection information is critical when sharing LiDAR data Allows users to perform 3D coordinate transformation directly on LiDAR (LAS) datasets MARS GeoCalc tool suite is written using a Blue Marble Geographics SDK, which allows users to reproject LAS point cloud data (horizontal & vertical) PREXXXX 12
13 Return Information Cross-section of trees, showing all return values PREXXXX 13
14 First, Second, Third, Fourth Returns First Returns Second Returns Third Returns Fourth Returns PREXXXX 14
15 Flightline Information View by flightline, unique flightline ID s View by flightline, non-unique flightline ID s PREXXXX 15
16 Flightline Information Flightline information allows for a quality control check to be performed in overlap data If a shift is detected within a flightline, this shift can be corrected is flightline information is retained You should request unique flightline information in you dataset Unique Flightline ID s. Flightline ID 4 (pink) is shifted +1 foot. Flightline ID 5 (yellow) is not shifted. This data can be corrected. Non-Unique Flightline ID s. Flightline ID 1 (green) is shifted +1 foot. Flightline ID 1 (green) is not shifted. This data cannot be corrected. PREXXXX 16
17 Quality Control Approaches PREXXXX 17
18 Overall QC Approach Relative accuracy of project flight lines drives absolute accuracy of the entire project! PREXXXX 18
19 Visual Analysis Methods PREXXXX 19
20 Color by Z Delta LiDAR point rendering designed to highlight vertical difference in adjacent flight lines Allows user a quick visual analysis of flightline overlap areas, which may indicate data calibration issues PREXXXX 20
21 Color by Z Delta Rendering Vertical Increment = 2.0 Vertical Increment = 0.25 PREXXXX 21
22 Flightline Separation Data export option intended to be used over an entire project area Export is a (compressed) JPEG2000 raster image; transparency imposed on image to allow for feature definition visibility Allows rapid examination of elevation consistency and is highly useful for boresighting activities PREXXXX 22
23 Flightline Separation Results PREXXXX 23
24 Visual Hillshade Analysis (Ground) Allows users to visually inspect the ground classification for pits or spikes. Quickly identifies the effectiveness of bareearth extraction capabilities of the vendor. Points rendered by data classification Hillshade image of the ground class PREXXXX 24
25 Profile View Profile of Ground & Vegetation Classes PREXXXX 25 Profile of Ground Class
26 Profile by Flightline Allows the user to compare overlapping flight lines to visually determine separation between them. Indicates the relative accuracy between flight lines, which can identify data calibration issues. PREXXXX 26
27 Contour by Flightline Allows the user to visually compare the contours from overlapping flight lines for coincidence. Well-calibrated datasets will display contours that appear almost identical. PREXXXX 27
28 Statistical Analysis Methods PREXXXX 28
29 LAS File Statistics The most common method to analyze LiDAR data deliverables is to review the statistics of the point cloud. Z min & Z max provides insight into data filtering results Point Density Average Ground Sample Distance (GSD) Return Information (1 st, 2 nd, 3 rd, etc.) Data Classifications has the data been classified into the specified classes? Flightline information is it present? Statistics allow users to thematically map results in GIS applications, which can help identify problem areas or data anomalies PREXXXX 29
30 LAS File Statistics Good Negative Z??? PREXXXX 30
31 Control Report To verify compliance to the project s vertical accuracy specification, users typically compare ground control checkpoints to the ground surface. American Society of Photogrammetry and Remote Sensing (ASPRS), National Map Accuracy Standards (NMAS) and National Standard for Spatial Data Accuracy (NSSDA) maintain their own vertical accuracy specifications Control Point File Format: Control_ID, X, Y, Z Example: 101, , , Can also be used to report the accuracy of contours generated from smoothed, gridded LiDAR data PREXXXX 31
32 Control Report Results PREXXXX 32
33 USGS LiDAR Specification PREXXXX 33
34 QC Foundation USGS LiDAR Spec USGS: The primary intent of this specification is to create consistency across all NGP-funded LiDAR collections, in particular those undertaken in support of the USGS National Elevation Dataset (NED). The USGS specification is the basis for most of the ARRAfunded LiDAR projects in the USA; often used as a SOW document for many non-arra funded LiDAR projects FEMA, NRCS, NOAA, USGS all have differing needs, so each of their requirements vary based on their importance to the contracting agency no consistency! PREXXXX 34
35 USGS LiDAR Specification Unlike most other LiDAR specs which focus on the derived bare-earth DEM product, this specification places unprecedented emphasis on the handling of the source LiDAR point cloud data. Defines minimum parameters for compliance; additional project upgrades listed (ex. increased vertical accuracy) Specification divided into four (4) main sections: Collection Data Processing and Handling Hydro-Flattening Requirements Deliverables PREXXXX 35
36 Automated QC Module PREXXXX 36
37 Automated QC Module The purpose of the quality control (QC) module is to provide an automated tool for verifying compliance of a LiDAR point cloud dataset to the LiDAR Guidelines and Base Specification (Version 13) from USGS. Supports 28 individual data QC standards; allows users to run any or all of the QC checks PDF output file reports on ALL points (no data sampling!); report includes tabular reports and supporting QC imagery Supports multi-core processing for more efficient throughput Default QC settings may be modified to support specific project specifications PREXXXX 37
38 QC Data Inputs Swath Data (LAS) Classified LiDAR files (LAS) Project Tile Scheme (ESRI shapefile) Project Boundary file (ESRI shapefile) Smoothed Best Estimate of Trajectory (SBET) file (ESRI shapefile) Ground Control Checkpoints (CSV file) PREXXXX 38
39 Collection Requirements PREXXXX 39 Returns (minimum of three) Intensity values Point Density / Nominal Point Spacing (NPS) Data Voids Spatial Distribution Verification Scan Angle Vertical Accuracy Relative Accuracy Flightline Overlap Collection Area (coverage check) Collection Conditions (weather)
40 Data Processing & Handling Requirements PREXXXX 40 LAS Format (v1.2 or v1.3) Waveform Data GPS Time Type Datums (horizontal & vertical) Projections Units of Measure File Sizes File Source ID Point Families (return information) Swath Coverage Noise Classes & Withheld Points Overlap Points Positional Accuracy Validation Classification Accuracy / Consistency Tiles
41 Hydro-Flattening Requirements Hydro-flattening pertains only to the creation of derived DEMs Utilizes linear breakline vectors to define boundary of hydrographic features (ex. ponds, lakes, etc.) Inland Ponds and Lakes Inland Streams and Rivers Non-Tidal Boundary Waters Tidal Waters PREXXXX 41
42 Data Deliverables Project Metadata (FGDC compliant, XML format) Collection Report Survey Report Processing Report QA / QC Report Raw Point Cloud Classified Point Cloud Bare-Earth Surface Breaklines Data QC: Independent 3rd-party QA / QC by external contractor is highly encouraged! PREXXXX 42
43 Spatial Distribution Verification Grid This tool is to used create a virtual grid that produces values representing the presence (value = 1) or absence (value = 0) of a first (of any) return LiDAR point within any portion of a predefined grid cell size. USGS specification requires At least 90% of the cells in the grid shall contain at least one (1) LiDAR point. Analysis process outputs a JPEG2000 image showing the valid cells (green) and the invalid cells (red). A report detailing the quantitative results (as a percentage) is also produced. PREXXXX 43
44 Spatial Distribution Verification Grid Data Export Dialog Analysis Results PREXXXX 44
45 QC Results Flightline Separation PREXXXX 45
46 Online LiDAR Resources Ver. 13 USGS LiDAR Guidelines and Base Specification FEMA Guidelines and Specifications for Flood Hazard Mapping Partners ASPRS LAS Specification ormat.html USGS Center for LiDAR Information Coordination and Knowledge (CLICK) PREXXXX 46
47 Online LiDAR Resources International LiDAR Mapping Forum (ILMF) SPAR Point Group LiDAR News National LIDAR Dataset (USA) USGS National Elevation Dataset (NED) PREXXXX 47
48 Data Demonstration Questions? PREXXXX 48
49 Contact Information Merrick & Company Merinda Lobato Tel: (303) 353- Bill Emison, MARS Product Manager Tel: (303) PREXXXX 49
50 Contact Information Merrick & Company website Booth 42 Merinda Lobato Bill Emison, MARS Product Manager Direct Telephone: (303) Mobile Telephone: (303) PREXXXX 50
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